Investigating Cellular Responses to their Mechanical Environment Using Proximity Labeling

A close relationship exists between the mechanical environment and cellular function. Properties of the mechanical environment such as substrate stiffness guide cellular functions in both physiological and pathological contexts through mechanotransduction. Cells orchestrate an appropriate cellular response to their mechanical environment through actin cytoskeleton reorganization and localization of mechanosensitive proteins. While select mechanosensitive proteins have been investigated, a study that broadly screens for changes in protein localization in response to substrate stiffness has not been performed. In this work, a new experimental workflow is developed where cells are plated onto polydimethylsiloxane substrates of varying stiffness and changes in morphology and cytoskeletal organization are investigated. Proximity labeling experiments are performed to screen for changes in protein localization in response to substrate stiffness. A nuclear enrichment analysis is applied to validate results of protein localization obtained through mass spectrometry. This work ultimately assists in improving our understanding of fundamental mechanisms of mechanotransduction

Conceptualizing, Applying and Evaluating SecMLOps: A Paradigm for Embedding Security into the ML Lifecycle

As machine learning (ML) systems become increasingly integrated into critical infrastructure and decision-making processes, ensuring their security and reliability is of paramount importance. However, traditional DevOps practices often fall short in addressing the unique security challenges posed by ML systems. This thesis introduces SecMLOps, a novel paradigm that explicitly integrates security considerations throughout the MLOps lifecycle to develop more secure, reliable, and trustworthy ML systems. The research begins with a comprehensive analysis of the DevOps landscape using a multivocal literature review methodology, providing a structured understanding of the various specialized DevOps variants and their relationships. Building upon these insights, the SecMLOps paradigm is conceptualized, examining its key aspects of people, technology, processes, governance, and compliance. To operationalize SecMLOps, a comprehensive framework is developed, integrating critical security activities into the MLOps workflow and assigning specific roles and responsibilities across the lifecycle stages. The effectiveness of the SecMLOps framework is evaluated through a real-world use case on a state-of-the-art pedestrian detection system, demonstrating its robustness against prevalent security threats such as data poisoning and adversarial examples. This thesis critically assesses the practical impact and challenges of adopting SecMLOps, considering factors such as organizational transformation, process integration, and resource implications. The applicability and generalizability of the framework across different domains are discussed, emphasizing its inherent adaptability while acknowledging the need for domain-specific considerations. By providing a structured approach to integrating security into MLOps and validating its effectiveness, this thesis advances the state of the art in secure ML development and deployment. The SecMLOps paradigm empowers organizations to proactively address security challenges, ensuring the integrity and trustworthiness of their ML systems in the face of evolving threats. As ML continues to expand into critical domains, the contributions of this thesis serve as a foundation for future research and practice in developing secure and reliable ML systems

Evaluation of Surfactant-Enhanced Cleaning Solutions for Humic Acid and BSA Fouling on Ceramic Membranes

Chemically enhanced backwash (CEB) has achieved extensive application, offering substantial improvements in fouling mitigation and enhancing membrane performance and longevity. Two commercially available non-ionic surfactants, Tween 80 and Triton X100, were employed at their CMC and combined with conventional cleaning solutions to evaluate NOM fouling and cleaning at different concentrations, utilizing membrane permeability, resistance in series (RIS), carbon mass balance, and contact angle. The use of surfactants in CEB dropped the contact angle by 20%. The transmembrane pressure (TMP) with Tween 80 and Triton X-100 based CEB solutions were in the range of 83-100 kPa and 88-95 kPa with the medium concentrations (CEBMTw & CEBMTx) compared to DI which was 128 kPa for HA and BSA. Tween-based CEBs exhibited a 50% reduction in fouling resistance on HA and 90% on BSA when medium and high concentrations (CEBMTw and CEBHTw) were employed, in comparison to hydraulic (DI) backwash

Impacts of Early Life Seizures on Memory Specificity

Memory specificity is the ability to recall an event and its specific details. This function is dependent on the ability of the dorsal CA1 (dCA1) of the hippocampus to encode and recall a sparse memory engram. Additionally, engram cells must be responding to contextual relevant cues, rather than random intrinsically driven activation. We hypothesis that an early life seizure (ELS) can induce a chronic upregulation in dCA1 activity, preventing the encoding and recall of sparse memory engrams and the loss of memory specificity. We found that a seizure at P10 significantly increases the baseline activation of the dCA1 at P24. Additionally, seizure mice also display a loss of memory specificity and show significantly larger memory engrams in the dCA1 following memory specificity testing, suggesting that large populations of neurons allocated to the memory engram are not encoding for contextually relevant stimuli

A Comprehensive Energy Management Framework for Electric Vehicle Driving Range Extension Considering Battery Aging

The longer charging time and scarcity of fast-charging stations contribute to range anxiety, a significant obstacle to the widespread adoption of electric vehicles (EVs). In addition, the battery pack of an electric vehicle is both expensive and has a limited lifespan. Reduced range and lower resale value are concerns for potential EV owners due to the degradation of capacity over time. The thesis puts forward a novel Energy Management Strategy (EMS) framework that is specifically developed to optimize the speed profile in real-time, thereby extending the driving range and battery lifespan of EVs. The contributions of this study include the integration of battery degradation considerations into the EMS of EVs, eliminating the reliance on precise mathematical modeling through the use of machine learning (ML) techniques, the introduction of driver-adjustable power-saving modes, and the execution of long-term performance analysis simulating up to one year of driving under varying temperatures and driving cycles. The proposed EMS framework is developed based on an autonomous EV platform, facilitating the optimization of the vehicle’s speed profile. However, it can also be adapted for human-driven vehicles by translating the throttle angle into torque demand. The subject of this research is a single-source-powered Battery EV (BEV). The proposed EMS utilizes Multi-Objective Genetic Algorithm (MOGA), Model Predictive Control (MPC), and Pontryagin’s Minimum Principle (PMP) as optimization techniques in three distinct models. The study develops a longitudinal vehicle model, maps motor-inverter characteristics based on experimental tests, and builds battery State of Charge (SOC) and State of Health (SOH) models using ML algorithms. The EMS framework is tested on identical EVs across various driving cycles and temperatures, and is compared against a reference model to assess its effectiveness. Results demonstrate that the proposed EMS, in its moderate mode, can increase driving range by 10.9%, reduce power consumption by 11%, and mitigate battery aging by 15.3%. In aggressive power-saving mode, the EMS extends the driving range by up to 17.7%, minimizes consumption by 16.7%, and improves battery SOH by 21.9%. These findings confirm that the proposed EMS framework can significantly enhance EV’s performance without major compromises to driving experience

Optimal Placement and Resource Provisioning for Virtual Network Functions: Traditional and Deep Reinforcement Learning Techniques

Network Function Virtualization (NFV) marks a fundamental shift in network architecture, enabling the decoupling of network functions from proprietary hardware onto standard computational platforms, thereby enhancing network flexibility, scalability, and cost-effectiveness. This thesis explores the complex dynamics of Virtual Network Function (VNF) placement, which is paramount to realizing the full potential of NFV. The challenge of optimal VNF placement lies in balancing resource utilization, performance, and cost, which has profound implications on network efficiency and service quality. This dissertation addresses five key research problems within the domain of NFV, each pertaining to different aspects of VNF deployment and management. Firstly, it investigates strategies for optimizing VNF placement to coordinate the interplay between network resources and performance demands. This includes developing and evaluating offline and online algorithms tailored for real-world network scenarios. Secondly, the thesis tackles the issue of feasibility restoration in network optimization, proposing algorithms that can automatically detect and correct infeasibilities in VNF placement, ensuring continual service availability and compliance with network policies. Thirdly, further expanding on the theme of network optimization, this work delves into the planning of service function chains, integrating both predictive and reactive strategies to manage network resources efficiently across service providers and infrastructure providers. Applying Deep Reinforcement Learning (DRL) combined with Reward-Constrained Policy Optimization (RCPO) forms the core of the fourth study area, focusing on the dynamic optimization of online VNF placement to adapt effectively to changing network conditions and operational constraints. Lastly, the thesis introduces a novel approach to addressing resource fragmentation through a DRL-based fragmentation-aware VNF placement strategy. This method seeks to optimize the utilization of fragmented resources, enhancing overall network performance and efficiency. Collectively, the overall research is supported through theoretical analysis, algorithmic design, and extensive simulations, leading to several publications that contribute to the advancement of knowledge in network function virtualization. The findings offer practical algorithms and frameworks for enhancing VNF placement strategies and pave the way for future innovations in network management and optimization within the rapidly evolving landscape of NFV

Unequal Access : Categorising Refugees in European Resettlement and Humanitarian Admission Programmes

This book is available in open access to all readers. Please download the book and consult the copyright page and acknowledgements to determine the organizations responsible for financially supporting that access, in addition to McGill-Queen's University Press (https://www.mqup.ca) and Local Engagement Refugee Research Network (https://carleton.ca/lerrn) with the assistance of the Carleton University Library. The series editors would really appreciate hearing about readers' experiences and uses of this edition of the ebook. To share, please write to [email protected] European states tighten their borders, refugees are regularly forced to take costly and highly dangerous routes to seek protection, sometimes with fatal consequences. The resettlement and humanitarian admission programmes that remain allow only a small number of migrants to enter directly from first countries of refuge. With less than 1 per cent of the world’s refugees resettled, such programs are extremely limited, forcing admission states and other actors to prioritize some groups and individuals over others. Unequal Access analyzes these dynamics and the complex boundaries of inclusion and exclusion they produce. Focusing on Europe and programs admitting people to Germany from Lebanon and Turkey, Natalie Welfens explores multilevel policy developments, from the national to the global. She follows the admission chain – from policy formulation, via refugee selection and pre-departure preparations, to refugee reception – and illustrates how policy categories transform based on intersecting social markers such as nationality, gender, and age. Unequal Access reveals the inequalities embedded in the categorization practices of resettlement and humanitarian admission programs, demonstrating how these practices profoundly shape access to protection for refugees

Exploring Audience Engagement with Sustainability Influencers: Insights from Two Field Studies

Influencer marketing has become a powerful tool across various industries. Sustainability influencers, who promote eco-friendly lifestyles on social media, have gained significant attention from environmentally conscious audiences. This thesis investigates audience engagement with five categories of sustainability influencers: Zero Waste, Ethical Fashion, DIY and Upcycling, Minimalism, and Vegan influencers. Through two field studies conducted on YouTube, this thesis aims to provide valuable insights into effective audience engagement strategies. Study 1 reveals that higher language arousal, paired with an educational appeal, significantly increases both passive and active engagement. Study 2 uses topic modeling to analyze audience comments to identify key drivers of active engagement, which include influencer expertise, attractiveness, parasocial relationships, and audience motivations such as information seeking, social interaction, and community identification. This thesis contributes practical strategies and academic insights into how sustainability influencers can effectively engage their audiences and encourage sustainable practices

Knowledge, Power, and Migration : Contesting the North/South Divide

As the field of migration studies has grown, the asymmetrical relationship between researchers in the Global North and in the South has produced a body of work that centres the concerns of the former. Those from the Global North and wealthier countries continue to produce the greater portion of this research, while research from Global South scholars with lived experiences as migrants is received as anecdotal or too niche to have universal application. Knowledge, Power, and Migration assembles researchers from across the divide to question the ways in which research practices can change the conversation on immigration. It encourages a necessary curiosity about how scholarship in the field can shape global, social, and epistemic justice. Migration is a constant in human history, but the sharp decline in permanent resettlement options, increasingly selective criteria, and violent enforcement measures of the twenty-first century constitute a crisis of immigration policy. Only by redressing the inequalities it shares with global governance structures can the discipline confront this historic challenge. Research on immigration can occasion reflections and practices that challenge epistemic injustices. Knowledge, Power, and Migration contributes to this ongoing project while offering insights on the practical organization of new forms of dialogue on migration in a largely unequal world.Free access to this e-book is available to readers, scholars, and students located in the Global South whose institutions lack the resources to purchase access to these books as well as to those in other regions who are part of non-profit or community organizations concerned with displacement and who lack alternate forms of access to the book or the resources needed to purchase these publications. Please see full access conditions below